Center for Machine Learning in Urology

项目总结 我们建议在儿童医院建立良性泌尿外科跨学科研究的探索性中心。 费城医院(CHOP)和宾夕法尼亚大学(宾夕法尼亚)，其中心使命是 应用机器学习来提高对病理生理学、诊断、风险分层和 儿童和成人良性泌尿系统疾病治疗反应的预测。建议数 CHOP/宾夕法尼亚大学泌尿学机器学习中心(CMLU)解决了关键的结构和科学障碍 这阻碍了良性疾病新疗法的开发和现有疗法的有效应用 终生患有泌尿系疾病。从结构上讲，泌尿科的研究是孤立进行的，彼此之间的互动很少 研究不同疾病或不同人群(例如，儿科和成人)的研究人员。从科学上讲， 成像和其他类型的复杂数据的分析受到观察者间的可变性和不完全利用的限制 可获得的信息。该提案通过将尖端方法应用于 机器学习用于分析用于评估肾结石患者的常规CT图像 疾病。CHOP/Penn CMLU的核心是泌尿科医生和机器学习专家的合作伙伴关系， 这将带来一种新的方法来产生知识，促进研究和临床护理。此外, CMLU将通过提供一个研究平台和独立的平台来扩大泌尿科研究社区 可应用于其他数据集的机器学习可执行文件。中心的使命将会实现 通过以下目标，并通过系统评价评估进展情况：目标1.扩大 研究良性泌尿系统疾病的研究基地。我们将与研究人员建立一个社区 基地，特别是与Kure，UroEpi计划，其他P20中心，和O‘Brien中心。我们将建造这座建筑 通过提供小型指导诊所，促进机器学习在个别项目中的应用， 开发一个与研究基地同步和异步参与的教育中心，以及 为所有机器学习工具免费提供所有源代码和独立可执行文件。目标2.目标 利用CT图像的机器学习改进输尿管结石通道的预测。CMLU拥有 开发了深度学习方法，对尿路结石和邻近结石进行分割和自动化测量 肾脏解剖。在研究项目中，我们将把这些方法与现有的分割方法进行比较，并 目前手工测量的黄金标准。然后我们将从数以千计的 CT扫描预测儿童和成人输尿管结石自然排出的概率 在印章和宾夕法尼亚大学的医疗系统中。目标3：促进良性泌尿系统疾病的合作 通过教育充实计划，研究不同级别的培训和中心。我们会 通过提供暑假，扩大各机构之间的互动，并吸引当地和全国的调查人员 研究实习，机构间交流计划，以及年度研究研讨会。

PROJECT SUMMARY We propose to establish an Exploratory Center for Interdisciplinary Research in Benign Urology at the Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania (Penn), the central mission of which is to apply machine learning to improve the understanding of the pathophysiology, diagnosis, risk stratification, and prediction of treatment responses of benign urological disease among children and adults. The proposed CHOP/Penn Center for Machine Learning in Urology (CMLU) addresses critical structural and scientific barriers that impede the development of new treatments and the effective application of existing treatments for benign urologic disease across the lifespan. Structurally, urologic research occurs in silos, with little interaction among investigators that study different diseases or different populations (e.g. pediatric and adult). Scientifically, analysis of imaging and other types complex data is limited by inter-observer variability, and incomplete utilization of available information. This proposal overcomes these barriers by applying cutting-edge approaches in machine learning to analyze CT images that are routinely obtained for evaluation of individuals with kidney stone disease. Central to the CHOP/Penn CMLU is the partnership of urologists and experts in machine learning, which will bring a new approach to generating knowledge that advances research and clinical care. In addition, the CMLU will expand the urologic research community by providing a research platform and standalone machine learning executables that could be applied to other datasets. The Center’s mission will be achieved through the following Aims, with progress assessed through systematic evaluation: Aim 1. To expand the research base investigating benign urological disease. We will establish a community with the research base, particularly with the KURe, UroEpi programs, other P20 Centers, and O’Brien Centers. We will build this community by providing mini-coaching clinics to facilitate application of machine learning to individual projects, developing an educational hub for synchronous and asynchronous engagement with the research base, and making freely available all source codes and standalone executables for all machine learning tools. Aim 2. To improve prediction of ureteral stone passage using machine learning of CT images. The CMLU has developed deep learning methods that segment and automate measurement of urinary stones and adjacent renal anatomy. In the Research Project, we will compare these methods to existing segmentation methods and the current gold standard of manual measurement. We will then extract informative features from thousands of CT scans to predict the probability of spontaneous passage of ureteral stones for children and adults evaluated in the CHOP and Penn healthcare systems. Aim 3. To foster collaboration in benign urological disease research across levels of training and centers through an Educational Enrichment Program. We will amplify interactions across institutions and engage investigators locally and nationally by providing summer research internships, and interinstitutional exchange program, and an annual research symposium.